Method and system for area management in smart city based on internet of things

ABSTRACT

The present disclosure provides a method and a system for area management in a smart city based on an Internet of Things. The method includes obtaining environmental monitoring data in a target area through a sensor network platform, the environmental monitoring data including at least one of air quality data, weather data, and satellite image data, predicting an air pollution situation in the target area through a regional prediction model based on the environmental monitoring data, the regional prediction model being a machine learning model, and sending prompt information, which is determined based on the air pollution situation in the target area, to a user platform through a service platform, wherein the target area is a hexagonal area; and the regional prediction model includes seven regional models and an air quality determination model; wherein a prediction mode of the regional prediction model is an iterative prediction.

CROSS-REFERENCE TO RELATED DISCLOSURES

This application is a continuation of U.S. application Ser. No.17/810,616, filed on Jul. 4, 2022, which claims priority to ChinesePatent Application No. 202210559574.7, filed on May 23, 2022, the entirecontents of each of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to the field of Internet of Things andair quality monitoring, and in particular to methods and systems forarea management in a smart city based on an Internet of Things.

BACKGROUND

With the development of society, air quality is more and more concernedby the public. There are many factors that affect air quality, such asvehicle exhaust emissions, factory exhaust emissions, dust fromconstruction sites, excavation operations, and sandstorms. Especially incities, the air pollution will be different in different areas.Predicting future air quality changes in an area can help regulate andcoordinate motor vehicle flow in urban transportation networks. Forareas with severe air pollution, it is also an important issue toinvestigate potential air quality pollution sources.

Therefore, it is desirable to provide a method and a system for areamanagement in a smart city based on an Internet of Things, which canpredict the air quality information in different areas and investigatepotential pollution sources.

SUMMARY

One or more embodiments of the present disclosure provide a method forarea management in a smart city based on an Internet of Things, and themethod is executed by a management platform. The method includes:obtaining environmental monitoring data in a target area through asensor network platform, the environmental monitoring data including atleast one of air quality data, weather data, and satellite image data,predicting an air pollution situation in the target area through aregional prediction model based on the environmental monitoring data,the regional prediction model being a machine learning model, andsending prompt information, which is determined based on the airpollution situation in the target area, to a user platform through aservice platform, wherein the target area is a hexagonal area, and aninput of the regional prediction model includes features of thehexagonal area and other hexagonal areas adjacent to the hexagonal area;the features include time-series features of the hexagonal area and theother hexagonal areas, and the time-series features include a timefeature, a traffic feature, and a meteorological feature; the regionalprediction model includes seven regional models and an air qualitydetermination model, the seven regional models respectively correspondto the hexagonal area and the other hexagonal areas adjacent to thehexagonal area, each of the seven regional models processes thetime-series feature of the corresponding hexagonal area to output acorresponding time-series feature vector, and the air qualitydetermination model processes the time-series feature vectors output bythe seven regional models to determine the pollution situation of thetarget area; wherein a prediction mode of the regional prediction modelis an iterative prediction, and the iterative prediction includes:iteratively predicting air pollution situation in the hexagonal area andthe other hexagonal areas adjacent to the hexagonal area at a secondtime point through the regional prediction model based on air pollutionsituation in the hexagonal area and the other hexagonal areas adjacentto the hexagonal area at a first time point; and the regional predictionmodel is obtained by joint training based on training data and a label,wherein the training data includes sample time-series features of asample hexagonal area and other sample hexagonal areas adjacent to thesample hexagonal area and sample time-series feature vectors, and thelabel is an actual air quality in a sample target area.

One or more embodiments of the present disclosure provide a system forarea management in a smart city based on an Internet of Things, and thesystem includes a user platform, a service platform, a managementplatform, a sensor network platform, and an object platform. The sensornetwork platform is configured to obtain environmental monitoring datain a target area through a sensor network platform, the environmentalmonitoring data including at least one air quality data, weather data,and satellite image data, the management platform is configured topredict an air pollution situation in the target area through a regionalprediction model based on the environmental monitoring data, theregional prediction model being a machine learning model, and theservice platform is configured to send prompt information, which isdetermined based on the air pollution situation in the target area, to auser platform through a service platform, wherein the target area is ahexagonal area, and an input of the regional prediction model includesfeatures of the hexagonal area and other hexagonal areas adjacent to thehexagonal area; the features include time-series features of thehexagonal area and the other hexagonal areas, and the time-seriesfeatures include a time feature, a traffic feature, and a meteorologicalfeature; the regional prediction model includes seven regional modelsand an air quality determination model, the seven regional modelsrespectively correspond to the hexagonal area and the other hexagonalareas adjacent to the hexagonal area, each of the seven regional modelsprocesses the time-series feature of the corresponding hexagonal area tooutput a corresponding time-series feature vector, and the air qualitydetermination model processes the time-series feature vectors output bythe seven regional models to determine the pollution situation of thetarget area; wherein a prediction mode of the regional prediction modelis an iterative prediction, and the iterative prediction includes:iteratively predicting air pollution situation in the hexagonal area andthe other hexagonal areas adjacent to the hexagonal area at a secondtime point through the regional prediction model based on air pollutionsituation in the hexagonal area and the other hexagonal areas adjacentto the hexagonal area at a first time point; and the regional predictionmodel is obtained by joint training based on training data and a label,wherein the training data includes sample time-series features of asample hexagonal area and other sample hexagonal areas adjacent to thesample hexagonal area and sample time-series feature vectors, and thelabel is an actual air quality in a sample target area.

One or more embodiments of the present disclosure provide anon-transitory computer-readable storage medium for storing computerinstructions, when reading the computer instructions in the storagemedium, a computer implements the above methods for predicting airquality.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be further described in the form ofexemplary embodiments, and these exemplary embodiments will be describedin detail with the diagrams. These embodiments are not restrictive. Inthese embodiments, the same number represents the same structure,wherein:

FIG. 1 illustrates a schematic diagram of an application scenario of amethod for predicting air quality in a smart city according to someembodiments of the present disclosure.

FIG. 2 illustrates a schematic diagram of a system for predicting airquality in a smart city according to some embodiments of the presentdisclosure.

FIG. 3 illustrates an exemplary flowchart of a method for predicting airquality in a smart city according to some embodiments of the presentdisclosure.

FIG. 4 illustrates an exemplary schematic diagram of a structure of aregional prediction model according to some embodiments of the presentdisclosure.

FIG. 5 illustrates an exemplary flowchart for determining a suspiciousarea according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In order to more clearly illustrate the technical solutions of theembodiments of the present disclosure, the following will brieflyintroduce the drawings that need to be used in the descriptions of theembodiments. Obviously, the drawings in the following descriptions areonly some examples or embodiments of the present disclosure. For thoseof ordinary skill in the art, without creative work, the presentdisclosure can be applied to the other similar application scenariosaccording to these diagrams. Unless it is obvious from the languageenvironment or otherwise stated, the same reference numbers in thedrawings represent the same structure or operation.

It should be understood that the “system”, “device”, “unit,” and/or“module” used herein is a method for distinguishing differentcomponents, elements, parts, parts, or assemblies of different levels.However, if other words can achieve the same purpose, the words can bereplaced by other expressions.

As shown in the present disclosure and the claims, unless the contextclearly suggests exceptional circumstances, the words “a”, “an”, “an”and/or “the” do not specifically refer to the singular, but also includethe plural. Generally speaking, the terms “including” and “contain” onlysuggest that the operations and elements that have been clearlyidentified are included, and these operations and elements do notconstitute an exclusive list, and the method or device may also includeother operations or elements.

Flowcharts are used in the present disclosure to illustrate theoperations performed by the system according to the embodiments of thepresent disclosure. It should be understood that the preceding orfollowing operations are not necessarily performed precisely in order.Instead, the individual operations can be processed in reverse order orsimultaneously. At the same time, users can also add other operations tothese processes, or remove an operation or several operations from theseprocesses.

FIG. 1 illustrates a schematic diagram of an application scenario of amethod for predicting air quality in a smart city according to someembodiments of the present disclosure.

As shown in FIG. 1 , the application scenario 100 of a method forpredicting air quality in a smart city may include a processor 110, anetwork 120, a database 130, a collection terminal 140, and a userterminal 150. In some embodiments, the components in the applicationscenario 100 may connect and/or communicate with each other through thenetwork 120 (e.g., a wireless connection, a wired connection, or acombination thereof). For example, the processor 110 may be connected tothe database 130 through the network 120. For another example, the userterminal 150 may be connected to the processor 110 and the database 130through the network 120.

The processor 110 may be used for processing information and/or datarelated to the application scenario 100, for example, predicting airquality results, generating prompt information or preferentialinformation instructions, etc. In some embodiments, the processor 110may include one or more processing engines (e.g., a single-chipprocessing engine or a multi-chip processing engine). For an exampleonly, the processor 110 may include a central processing unit (CPU). Theprocessor 110 may process data, information and/or processing resultsobtained from other devices or system components and execute programinstructions based on such data, information and/or processing resultsto perform one or more functions described in the present disclosure.

The network 120 may connect various components of the applicationscenario 100 and/or connect the application scenario 100 with externalresource parts. The network enables communication between the variouscomponents and between other components outside the application scenario100 to facilitate the exchange of data and/or information. The networkmay be a local area network, a wide area network, the Internet, or thelike, or any combination thereof. The database 130 may be used to storedata and/or instructions. In some embodiments, the database 130 maystore data and/or instructions that processor 110 performs or uses toaccomplish the exemplary methods described in the present disclosure. Insome embodiments, the database 130 may be connected to the network 120to communicate with one or more components of application scenario 100(e.g., the processor 110, the user terminal 150).

The collection terminal 140 may be used to collect data and/orinformation. For example, air quality, weather data, captured images,etc. Exemplarily, the collection terminal 140 may include a weathersatellite 140-1, an air quality detection device 140-2, a land satellite140-3, etc. In some embodiments, the collection terminal may send thecollected data and/or information to the processor through the network.

The user terminal 150 may include one or more terminal devices orsoftware. In some embodiments, the user terminal 150 may include amobile phone 150-1, a tablet computer 150-2, a laptop computer 150-3,etc. In some embodiments, the user may view information and/or enterdata and/or instructions through the user terminal. For example, theuser may view the information of vehicles that often drive into acertain area through the user terminal. For another example, the usermay input an instruction for sending a short message through the userterminal, send preferential information to the driver for guiding thedriver to change the route, or remind the driver not to go to an areawith severe air pollution.

It should be noted that the application scenario 100 is provided forillustrative purposes only and is not intended to limit the scope ofthis application. For those skilled in the art, various modifications orchanges may be made based on the description of the present disclosure.For example, the application scenario 100 may also include informationsources. However, such changes and modifications do not depart from thescope of the application.

The Internet of Things system is an information processing system thatincludes part or all of a user platform, a service platform, amanagement platform, a sensor network platform, and an object platform.The user platform is a functional platform that realizes user perceptioninformation obtention and control information generation. The serviceplatform may realize the connection of the management platform and theuser platform, which plays the functions of sensing information servicecommunication and controlling information service communication. Themanagement platform may realize the overall planning and coordination ofthe connection and cooperation between various functional platforms(e.g., the user platforms and the service platforms). The managementplatform gathers information about the operating system of the Internetof Things and may provide perception management and control managementfunctions for the operating system of the Internet of Things. Theservice platform may realize the connection of the management platformand the object platform, which plays the functions of sensinginformation service communication and controlling information servicecommunication. The user platform is a functional platform that realizesuser perception information obtention and control informationgeneration.

The processing of information in the Internet of Things system may bedivided into a processing flow of user perception information and aprocessing flow of control information. The control information may beinformation generated based on user perception information. In someembodiments, the control information may include user demand controlinformation, and the user perception information may include user queryinformation. The processing of perception information includes obtainingthe perception information and transmitting it to the managementplatform through the sensor network platform by the object platform. Theuser demand control information is transmitted from the managementplatform to the user platform through the service platform, therebyrealizing the control of sending prompt information.

In some embodiments, when the Internet of Things system is applied tocity management, it may be referred to as an Internet of Things systemin the smart city.

FIG. 2 illustrates a schematic diagram of a system for predicting airquality in a smart city according to some embodiments of the presentdisclosure. As shown in FIG. 2 , the system 200 for predicting airquality in a smart city may be implemented based on the Internet ofThings system. The system 200 for predicting air quality in a smart cityincludes a user platform 210, a service platform 220, a managementplatform 230, a sensor network platform 240, and an object platform 250.In some embodiments, the system 200 for predicting air quality in asmart city may be part of the processor 110 or implemented by theprocessor 110.

In some embodiments, the system 200 for predicting air quality in asmart city may be applied to various scenarios of air qualityprediction. In some embodiments, the system 200 for predicting airquality in a smart city may respectively obtain weather information,traffic information, or the like in various scenarios to obtain airquality prediction values in each scenario. In some embodiments, thesystem 200 for predicting air quality in a smart city may determine theair quality pollution source based on the obtained air qualityprediction values under various scenarios.

A plurality of scenarios of air quality prediction may includepredicting the air quality in target area, predicting the air quality inthe adjacent area of target area, comparing the predicted air qualitywith the actual air quality in the area, determining suspicious areabased on difference, investigating pollution sources in suspiciousareas, etc. It should be noted that the above scenarios are onlyexamples, and do not limit the specific application scenarios of thesystem 200 for predicting air quality in a smart city. Those skilled inthe art may apply the system 200 for predicting air quality in a smartcity to any other suitable scenarios on the basis of the contentdisclosed in the embodiment.

In some embodiments, when the system 200 for predicting air quality in asmart city is applied to the air quality prediction in a certain area,the management platform 230 obtains the area to be predicted, which maybe a hexagon, and at the same time, obtains the six hexagonal areasadjacent to the target area. The management platform 230 obtains therelevant data in the area to be predicted and the six adjacent hexagonalareas, the relevant data includes meteorological data (e.g., a windspeed, a wind direction, an air pressure, a humidity, a temperature,etc.) and traffic data (e.g., a traffic flow, congestion, etc.). Therelevant data may be data of a sequence at multiple historical timepoints (e.g., multiple time points in the past day or week at intervalsof 30 minutes or one hour) up to the current time, and the relevant datais processed through a regional prediction model to predict the airquality in the target area.

In some embodiments, when the system 200 for predicting air quality in asmart city is applied to the air quality prediction in the adjacentareas of the target area, iterative prediction may be performed througha regional prediction model. The iterative prediction may predict theair quality in each area at the next time point based on the predicteddata of the air quality in each area at a certain time point. Forexample, after predicting the air quality of each area at the first timepoint in the future, the relevant data at the first time point in thefuture will be used as the historical time point data at the second timepoint in the future, which is used as the input of the regionalprediction model for predicting the air quality at the second time pointin the future. For the air quality prediction in the target area, thepollution situation in adjacent areas is introduced for the iterativeprediction, which may make the air quality prediction in the target areamore accurate. Further, the six hexagonal areas may be predicted in turnand the predicted value of the air quality in the adjacent six hexagonalareas may be obtained, so as to obtain the air quality of each area in alarger range, causing that the accidental error of the data is reducedand the accuracy in the target area prediction is improved. For therelevant descriptions of air quality prediction, please refer to FIG. 5and its related descriptions thereof.

In some embodiments, the system 200 for predicting air quality in asmart city is applied to compare the predicted air quality with theactual air quality in the area and determining the suspicious area basedon the difference between the predicted air quality with the actual airquality in the area. The management platform 230 not only obtains theair quality prediction values in in the target area and the sixhexagonal areas adjacent to the target area, but also obtains the airquality true values in the target area and the six hexagonal areasadjacent to the target area. The air quality true values may be obtainedby the air quality detection device 140-2 installed in each area in theobject platform 250 and uploaded to the management platform 230 throughthe sensor network platform 240. The management platform 230 obtains adifference value of air quality by comparing the air quality predictionvalues in each area with the corresponding air quality true values anddetermines a suspicious area based on the difference value. There may beadditional pollution sources in the suspicious area.

In some embodiments, when the system 200 for predicting air quality in asmart city is applied to investigating pollution sources in suspiciousareas, the management platform 230 obtains multiple images in thesuspicious areas. The multiple images may be obtained by the landsatellite 140-3 in the object platform 250 and uploaded to themanagement platform 230 through the sensor network platform 240. Themanagement platform 230 may identify the multiple images through apollution source identification model, and determine the type of thepollution source, for example, the type of the pollution source may be avehicle source (e.g., the exhaust emission of a vehicle, and the airpollution includes the pollution of nitrogen dioxide, carbon monoxide,and PM 2.5), an industrial source (e.g., factory exhaust gas emissions,and air pollution includes sulfur dioxide pollution), a dust source(e.g., construction site dust, an excavation operation, a sandstorm, astraw burning, etc., and pollution includes particulate matterpollution), etc.

In other embodiments, when the system 200 for predicting air quality ina smart city is applied to investigating the pollution source in thesuspicious area, the management platform 230 may determine the suspectpoint through the emission determination model in combination with otherfeatures. The other features may be air quality features including PM2.5, PM 10, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxidecontent, etc. in the air. The suspect point may refer to the differencebetween the index of the air quality and the pollution index of the airquality caused by the pollution source. For example, for the suspiciousarea, the investigated pollution source is straw burning, which mainlyaffects the PM 2.5 index. If the ozone pollution index in the airquality data in this area is high, it means that there are otherpollution sources, which needs to continue to be investigated todetermine the ozone pollution source.

For those skilled in the art, after understanding the principle of thesystem, it is possible to move the system to any other suitable scenariowithout departing from this principle.

The following will illustrate a specific description of the system 200for predicting air quality in a smart city through taking the system 200for predicting air quality in a smart city applied to the air qualityprediction scenario in a certain area of the city as an example system200 for predicting air quality in a smart city.

The user platform 210 may refer to a user-led platform, including aplatform that obtains user needs and feeds back information to users. Insome embodiments, the user platform is configured to input aninstruction through the user terminal to query the air qualityinformation in a certain area of the city and also query theregistration information of vehicles that frequently drive into thearea. In some embodiments, the user platform is configured to displaythe queried vehicle registration information and the owner informationof the vehicle through the display terminal. In other embodiments, theuser platform is configured as a user terminal to obtain promptinformation or preferential information input by the user, which aresent to the target driver in the form of a short message.

The service platform 220 may refer to a platform that communicates userneeds and control information, which connects the user platform 210 andthe management platform 230. In some embodiments, the service platform220 is configured to obtain instructions issued by the user through theuser platform to query air quality information and vehicle registrationinformation in a certain area of the city and feedback the vehicleregistration information to the user. In some embodiments, the serviceplatform 220 may receive information from the management platform 230,and perform data processing operations such as extraction,classification, and reprocessing of the received information to generatevaluable information such as statistical data, trend data, andcomparison data, and provide users with corresponding services accordingto the needs of users. Exemplarily, when the user queries the airquality information in a certain area, the service platform 220 obtainsthe air quality prediction information in the area from the managementplatform 230 according to the user's request instruction, summarizes theair quality information in the adjacent surrounding areas of the area,and feeds it back to the user. In some embodiments, the service platform220 obtains the user's query instruction for suspicious air pollutionsources in a certain area and obtains the investigation results of thesuspicious air pollution sources from the management platform 230 tofeed back to the user.

The management platform 230 may refer to a platform for predicting airquality in different areas of the city. In some embodiments, themanagement platform 230 may be configured to obtain an area to bepredicted and an adjacent area, the area to be predicted may be ahexagon, and the adjacent area may be six adjacent hexagons. In someembodiments, the management platform 230 may be configured as a regionalprediction model to predict the air quality in the area to be predicted,and the input of the regional prediction model includes the features inthe hexagonal area and the six adjacent hexagonal areas.

In some embodiments, the features further include vehicle pollutiondata, and the method of the vehicle pollution data is obtained byoperations including: obtaining road situation prediction data andvehicle registration data in the hexagonal area and other hexagonalareas adjacent to the hexagonal area, predicting the vehicle pollutiondata in the hexagonal area and other hexagonal areas adjacent to thehexagonal area by processing the road situation prediction data and thevehicle registration data through a vehicle pollution model.

In some embodiments, the management platform 230 may be configured toiteratively predict the air pollution situation in the hexagonal areaand other hexagonal areas adjacent to the hexagonal area at a secondtime point through the regional prediction model based on the airpollution situation in the hexagonal area and other hexagonal areasadjacent to the hexagonal area at a first time point.

In some embodiments, the management platform 230 may be configured toobtain deviation data between the air pollution situation and an actualair pollution situation, and determine a suspicious area based on thedeviation data, the suspicious area being an area that includesadditional pollution sources.

In some embodiments, the management platform 230 may be configured tosend prompt information, which is determined based on the air pollutionsituation in the target area, to a user platform through the serviceplatform 220 includes: determining a target sub-area where the airpollution situation in the target area satisfies a preset condition,obtaining vehicle information entering the target sub-area through anobject platform 250, and sending the prompt information and preferentialinformation through the service platform 220 based on the vehicleinformation and the air pollution situation in the target area. In someembodiments, by sending information to vehicle owners who frequentlydrive into the target area, the vehicle owner is prompted to detour orchange the driving route, so as to avoid further deterioration of theair quality in the area. In some embodiments, the vehicle owners areattracted by sending preferential information, such as discounts at tollstations in other areas, discounts at gas stations, etc., to divert thetraffic flow on the road and alleviate the air pollution of the vehicleexhaust emissions caused by the vehicles in the target area.

The sensor network platform 240 may refer to a functional platform thatmanages the communication of air quality predictions in the city. Insome embodiments, the sensor network platform 240 may connect themanagement platform 230 and the object platform 250 to implementcommunication functions. In some embodiments, a sensor network platformmay include multiple sensor network sub-platforms. Different sensornetwork sub-platforms provide corresponding communication channels fordifferent sensor devices. For example, the meteorological satellitesensor sub-platform communicates with the meteorological satellite toobtain the meteorological data of the meteorological satellite. In someembodiments, the sensor network platform 240 is divided into differentsensor network sub-platforms according to the types of differentcollection devices, so that the sensor network platform 240 is moretargeted for the obtention and processing of collected information. Atthe same time, multiple communication channels relieve the pressure ofdata uploading on the sensor network platform and improve the efficiencyof system operation.

The object platform 250 may refer to a platform for obtaining externalperception information through various sensor devices. For example, theobject platform 250 includes an air quality monitoring device installedin a certain area of the city for obtaining the air quality informationin the area and uploading it to the management platform 230 through thesensor network platform 240 to compare air quality prediction values.For another example, the object platform 250 includes meteorologicalsatellites, which are used to obtain meteorological data in a certainarea and upload it to the management platform 230 through the sensornetwork platform 240 to predict the air quality in the area. For anotherexample, the object platform 250 includes a Landsat, which is used toobtain an image in a certain area and upload it to the managementplatform 230 through the sensor network platform 240 to investigate thesuspicious air pollution sources.

It should be noted that the above descriptions of the system and itscomponents is only for the convenience of description, and not limit thepresent disclosure to the scope of the illustrated embodiments. It maybe understood that for those skilled in the art, after understanding theprinciple of the system, it is possible to arbitrarily combine thevarious components, or form a subsystem to connect with other componentswithout departing from the principle. For example, each component mayshare a database, and each component may also have its own database.Such deformations are within the scope of protection of the presentdisclosure.

FIG. 3 illustrates an exemplary flowchart of a method for predicting airquality in a smart city according to some embodiments of the presentdisclosure. As shown in FIG. 3 , the process 300 includes the followingoperations. In some embodiments, the process 300 may be performed by amanagement platform.

In operation 310, environmental monitoring data may be obtained in atarget area through a sensor network platform, and the environmentalmonitoring data includes at least one of air quality data, weather data,and satellite image data.

The target area may be a certain area for which air quality predictionis required. For example, the target area may be a city such asShanghai, or a certain area of the city such as the Jing'an District. Insome embodiments, the management platform may divide an area into aplurality of honeycomb-shaped hexagonal areas, and the target area maybe one or more hexagonal areas. In some embodiments, the target area maybe adjacent to other areas. For example, when one hexagonal area is usedas the target area, the target area may be adjacent to six otherhexagonal areas. In some embodiments, the target area may also haveother shapes. For example, the target area may also be a rectangle, andthe target area may be adjacent to eight other rectangular areas.

The environmental monitoring data may be monitored data related to theenvironment. For example, the environmental monitoring data may includeat least one of air quality data, weather data, and satellite imagedata.

The air quality data may be data reflecting the air pollution degree.For example, air quality data may include air pollution index, theconcentration of various pollutants (e.g., nitrogen oxides, carbonmonoxide, respirable particulate matter, etc.) in the air, etc.

The weather data may be data reflecting climate and weather in a certaintime and area. For example, the weather data may include temperature,humidity, precipitation, wind levels, etc.

The satellite image data may be data fed back by satellites that conductmeteorological observations on the earth. For example, the satelliteimage data may include image data such as satellite cloud images.

In some embodiments, the above-mentioned environmental monitoring datamay be obtained through different object platforms. For example, thesatellite image data may be obtained through a satellite platform, theair quality data may be obtained through an air quality monitoringplatform, the weather data may be determined through a meteorologicalwebsite or sensors related to the measurement of weather indicators,etc.

In operation 320, an air pollution situation in the target area may bepredicted through a regional prediction model based on the environmentalmonitoring data, and the regional prediction model is a machine learningmodel.

The regional prediction model may be a model for predicting the airpollution situation in the target area. For example, the regionalprediction model may be a Deep Neural Network (DNN) model, a RecurrentNeural Network (RNN) model, or the like, or any combination thereof.

The air pollution situation may be any information related to airpollution in the target area. For example, air pollution situation mayinclude types of pollution (e.g., industrial pollution, radioactivepollution, biological pollution, etc.), pollution sources (e.g.,chemical factory exhaust gas, straw combustion exhaust gas, etc.),pollution degree (e.g., urban air quality level, air pollutant contentsuch as PM 2.5 content, etc.), or the like. In some embodiments, the airpollution situation includes actual air quality obtained by an airquality sensor.

In some embodiments, the management platform may predict the airpollution situation in the target area based on the environmentalmonitoring data in various ways. For example, the air pollution in thetarget area may be predicted based on the mapping relationship betweenthe environmental monitoring data and the air pollution in the targetarea.

In some embodiments, the air pollution situation in the target area maybe predicted based on a regional prediction model, and the regionalprediction model may be a machine learning model.

More descriptions about the air pollution situation in the target areapredicted by the regional prediction model, please refer to FIG. 4 andthe relevant descriptions.

In operation 330, prompt information, which is determined based on theair pollution situation in the target area, may be sent to a userplatform through a service platform.

The prompt information may be information related to the air pollutionsituation in the target area. For example, the prompt information may beany form of information (e.g., text, image, voice, etc.) that reflectsthe pollution source, pollution type, pollution severity level, etc. inthe target area. In some embodiments, the prompt information may bepresented on the user terminal and sent to the user. In someembodiments, the prompt information may further include images or videosrelated to the pollution source, the type of the pollution source, thecorresponding pollution treatment method, etc.

In some embodiments, the prompt information may be generated by presetrules, for example, the prompt information may be a preset text rule of“pollution type+pollution source+pollution severity level”, such as“pollution type: biological pollution, pollution source: burning ofstraw in farmland, pollution severity level: severe.” In someembodiments, the prompt information may also include other forms ofpreset rules, such as preset rules related to images, preset rulesrelated to speech, etc. In some embodiments, the prompt information maybe sent from the service platform to the user platform through thenetwork.

The method for predicting air quality in a smart city based on theInternet of Things are described in some embodiments of the presentdisclosure, which may realize the air quality prediction based on themachine learning model, and the machine learning model may process theenvironmental monitoring data obtained by each platform and channel,improve the comprehensiveness of prediction and obtention of the airquality-related information, and improve the accuracy of results of theair quality prediction.

It should be noted that the above descriptions about the process 300 isonly for example and illustration, and not limit the scope of thepresent disclosure. For those skilled in the art, various modificationsand changes may be made to the process 300 under the guidance of thepresent disclosure. However, these corrections and changes are stillwithin the scope of the present disclosure. For example, the process 300may also include preprocessing operations.

FIG. 4 illustrates an exemplary schematic diagram of a structure of aregional prediction model according to some embodiments of the presentdisclosure. The structure 400 is shown in FIG. 4 . In some embodiments,the structure 400 may be implemented through a management platform.

In some embodiments, the regional prediction model may include at leastone regional model and one air quality judgment model, and each regionalmodel corresponds to one area. For example, at least one regional modelmay be seven regional models, namely a first regional model, a secondregional model, . . . , a seventh regional model, each model correspondsto one area, the seventh regional model may correspond to the targetarea, the first regional model, the second regional model . . . thesixth regional model may respectively correspond to other areas adjacentto the target area (that is, respectively correspond to the first area,the second area, . . . , the sixth area).

It can be understood that the descriptions of the order and quantity ofeach regional model in the present disclosure is intended to illustrate,and does not imply a limitation on the order, quantity, function, andcorresponding areas of the regional models. For example, the firstregional model may be the above-mentioned seventh regional model, thesecond regional model may correspond to the target area, etc. Foranother example, the quantity of the regional models may be determinedbased on the shape of the target area. For example, the shape of thearea may be a rectangle, and the quantity of the regional models may befive.

The input of the regional prediction model may include the features ofthe target area (hexagonal area) and the features of other hexagonalareas adjacent to the target area (hexagonal area), and the output ofthe regional prediction model may include the area air quality in thetarget area.

In some embodiments, the features of the target area (hexagonal area)may be the time-series features of the target area, and the features ofother hexagonal areas adjacent to the target area may be the time-seriesfeatures of other hexagonal areas. The time-series features may betraffic features and meteorological features of a certain area at eachtime point in a period of time, that is, the time-series features mayinclude time features, traffic features, and meteorological features.For example, 2 hours are set as a time step in a day, and a time pointis obtained every 2 hours. During the time period of the day, at eachtime point, traffic features (e.g., traffic flow, congestion, etc.),meteorological features (e.g., wind speed, wind direction, air pressure,humidity, temperature, etc.), and the corresponding time point (i.e.,time feature) as the input of the model. In some embodiments, thetime-series features may include a first time-series feature, a secondtime-series feature, . . . , a seventh time-series feature, etc., whichare used to represent corresponding time-series features in differentareas.

The first regional model may be a model for extracting features of thefirst area. For example, the first regional model may be a deep neuralnetwork model.

The input of the first regional model may include a time-series feature,and the output may include a first time-series feature vector.

The time-series feature vector may be a vector reflecting changes intraffic and meteorological features of an area within a certain timeperiod. For example, the elements of the vector may include a timepoint, and features such as traffic flow, congestion, wind speed, winddirection, air pressure, humidity, temperature, etc. at the time point.

In some embodiments, the regional prediction model may also include asecond regional model, a third regional model, . . . , a seventhregional model, etc. The above regional models correspond to differentareas, and the model types, input and output of the above regionalmodels may be similar to that of the first regional model, for example,the second regional model may be a deep neural network model, the inputof the second regional model may include time-series features, and theoutput may include second time-series feature vectors. To sum up, thetime-series feature vectors corresponding to the above seven areas maybe obtained through the above seven regional models. Each time-seriesfeature vector may reflect various features of the corresponding area,and the seventh time-series feature vector may correspond to the targetarea.

The air quality judgment model may be a model for confirming airpollution situations. For example, the air quality judgment model may bea deep neural network model, etc.

The input of the air quality judgment model may include theabove-mentioned first time-series feature vector, the second time-seriesfeature vector, . . . , the seventh time-series feature vector, and theoutput of the air quality judgment model may include the air pollutionsituation in the target area.

In some embodiments, the regional prediction model may be obtainedthrough joint training. For example, training sample data is input intothe first regional model, the second regional model, . . . , the seventhregional model, and the training sample data is the time-series featureof the corresponding area, and the output first time-series featurevector, second time-series feature vector, . . . , the seventhtime-series feature vector are obtained. Then, the above time-seriesfeature vector used as the training sample data of the air qualityjudgment model are input into the air quality judgment model to obtainthe air pollution situation in the target area, and the air pollutionsituation in the sample target area is used to verify the output of theair quality judgment model. For example, verification may be performedbased on the actual air quality obtained by the air quality detectiondevice installed in the target area as the label of the air qualityjudgment model.

For another example, the training sample data includes the time-seriesfeature and the time-series feature vector of the corresponding area,the training sample data is input into the first regional model, thesecond regional model, . . . , the seventh regional model, and thesample time-series feature vector is input into the air quality judgmentmodel, the outputs of the first regional model, the second regionalmodel, . . . , the seventh regional model are used as the input of theair quality judgment model, and the label is the air pollution situationin the target area. In some embodiments, the training labels may be theactual air quality obtained by the air quality sensor. During thetraining process, a loss function is established to update theparameters of the model based on the air pollution in the target areaand the outputs of the first regional model, the second regional model,. . . , and the seventh regional model.

In some embodiments, the training sample data may include at leasttime-series features of different areas. The labels may be the airpollution situation in the target area. The labels may be obtainedmanually.

In some embodiments, the input of the regional prediction model may alsoinclude vehicle pollution data (also referred to as vehicle pollutionfeatures). In some embodiments, obtaining the vehicle pollution dataincludes: obtaining road situation prediction data and vehicleregistration data in the hexagonal area and other hexagonal areasadjacent to the hexagonal area, and predicting the vehicle pollutiondata in the hexagonal area and other hexagonal areas adjacent to thehexagonal area by processing the road situation prediction data and thevehicle registration data through a vehicle pollution model.

The vehicle pollution data may be data of air pollution caused byvehicles in an area within a certain period of time. For example, thevehicle pollution data may include vehicle exhaust emission scale,vehicle exhaust emission composition, vehicle exhaust emissiondistribution, etc. In some embodiments, the vehicle pollution data maybe determined by a vehicle pollution model. In some embodiments, some orall of the vehicle pollution data may belong to time-series features.

The vehicle pollution model may be a model that determines vehiclepollution data. For example, the vehicle pollution model may be a deepneural network model, etc. In some embodiments, the vehicle pollutionmodel may include a first vehicle pollution model, a second vehiclepollution model, . . . , a seventh vehicle pollution model, etc., whichare used to represent models corresponding to vehicle pollution data indifferent areas, all of which belong to the same parameters model ofvehicle pollution.

The input of the vehicle pollution model may include road situationprediction data and vehicle registration data, and the output of thevehicle pollution model may include vehicle pollution data. In someembodiments, the output vehicle pollution data may be represented by airquality data, so the vehicle pollution data may also be understood asair quality data under conditions corresponding to the input roadsituation prediction data and vehicle registration data.

The road situation prediction data may be statistical data of roadsituation data within a certain period of time in history. For example,the road situation prediction data may include the count of vehicles onthe road, the degree of road congestion, etc. The road traffic dataincludes features such as exhaust emissions.

The vehicle registration data may be vehicle-related information withina certain period of time in history. For example, the vehicleregistration data may include the vehicle's power type (e.g., fuel,electric, etc.), exhaust emissions, fuel consumption, etc.

In some embodiments, the vehicle pollution model may be a DNN whoseinput may be road traffic data and the output may be air quality data(representing vehicle pollution data). The road traffic data includesfeatures such as road traffic flow, the proportion of fuel vehicles,exhaust emissions, etc.

In some embodiments, the vehicle pollution model may be obtained throughjoint training with the regional prediction model. For example, trainingsample data is input into the vehicle pollution model to obtain theoutput historical vehicle pollution data, the training sample data beinghistorical road situation prediction data and historical vehicleregistration data, then the above historical vehicle pollution data usedas the training sample data of the regional prediction model is inputinto the regional prediction model to obtain the air pollution situationin the target area, and the output of the regional prediction model isverified by using the air pollution situation in the sample target area.Using the back-propagation features of the neural network model, thevalidation data of the vehicle pollution data output by the vehiclepollution model is obtained, and the validation data is used as a labelto train the vehicle pollution model.

For another example, the training sample data includes historical roadsituation prediction data, historical vehicle registration data, andhistorical vehicle pollution data, the training sample data is inputinto the vehicle pollution model, the sample historical vehiclepollution data is input into the regional prediction model, and theoutput of the vehicle pollution model is used as the input of theregional prediction model, and the label is the air pollution situationin the target area. During the training process, a loss function isestablished based on the air pollution in the target area and the outputof the vehicle pollution model to update the parameters of the model.

In some embodiments, the training sample data may at least includehistorical road situation prediction data and historical vehicleregistration data. The label may be the air pollution situation in thetarget area. The label may be obtained by manual annotation ordetermined based on the monitoring of corresponding road sensors such asair quality sensors.

In some embodiments, the prediction approach of the regional predictionmodel may be iterative prediction, and the iterative predictionincludes: iteratively predicting the air pollution situation in thehexagonal area and other hexagonal areas adjacent to the hexagonal areaat a second time point through the regional prediction model based onair pollution situation in the hexagonal area and other hexagonal areasadjacent to the hexagonal area at a first time point.

The first time point may be the time point at which the air pollutionsituation prediction is initially performed within a certain timeperiod. The second time point may be the time point at which the airpollution situation prediction is performed again within a certain timeperiod. For example, the first time point may be expressed as a specifictime such as 9:00, or a specific day, and the second time point may alsobe expressed similarly.

In some embodiments, the iterative prediction may obtain the airpollution situation and the time-series features corresponding to thefirst time point from the prediction process at the first time point anduse the air pollution situation and the time-series featurescorresponding to the first time point as the input of the predictionprocess at the second time point. In some embodiments, the predictionprocess may further include a third time point, a fourth time point, . .. , etc. More descriptions about the prediction processes at the abovetime points, please refer to the prediction processes at the first timepoint and the second time point.

In some embodiments, the condition of the iterative termination may bethat the count of time points reaches a preset count of time points. Forexample, the preset count of time points is five, when the predictionprocess is the fifth time point, the iterative prediction stopped whenthe prediction corresponding to the time point ends.

The prediction of the air pollution situation in the future may berealized through the regional prediction model described in someembodiments of the present disclosure, the impact of vehicle exhaust onthe air instructions is considered by introducing the statistical dataof vehicles in the area. In addition, the model is obtained throughjoint training, which may reduce the training samples, simplify thetraining process, and avoid the problem that the labels of each regionalmodel are difficult to obtain.

FIG. 5 illustrates an exemplary flowchart for determining a suspiciousarea according to some embodiments of the present disclosure. As shownin FIG. 5 , the process 500 includes the following operations. In someembodiments, the process 500 may be performed by a management platform.

In operation 510, deviation data may be obtained between the airpollution situation and an actual air pollution situation.

The deviation data may be data reflecting that the predicted airpollution situation is different from the actual situation. For example,the deviation data may include that the PM 2.5 content in the predictedtime period deviates greatly from the actual PM 2.5 content in the timeperiod, and the degree of deviation exceeds the deviation threshold. Thedeviation data may also include that the pollution type in the predictedtime period is biological pollution, and the pollution types of theactual air pollution situation in the time period are biologicalpollution and industrial pollution.

In some embodiments, the deviation data may be obtained by comparing thepredicted air pollution situation with the actual air pollutionsituation through the management platform, and the actual air pollutionsituation may be obtained through an air quality detection device ormanual input.

In operation 520, a suspicious area may be determined based on thedeviation data.

The suspicious areas may be areas where other pollution sources may bepresent. For example, the suspicious area may be one or more of theabove-mentioned hexagonal areas, and the suspicious area may also be aspecific address, such as a factory.

In some embodiments, the suspicious areas may be determined fromenvironmental monitoring data. For example, the area where the hazeconcentration reaches the threshold of the haze concentration isdetermined by satellite image data as the suspicious area, or the areawhere the dust concentration in the air in the sensor detection areareaches the dust concentration threshold is regarded as the suspiciousarea. In some embodiments, the suspicious area may be determined fromvehicle-related data such as vehicle pollution data, road situationprediction data, etc. For example, through the distribution of the roadnetwork where vehicles travel, areas with high vehicle density aredetermined as suspicious areas. In some embodiments, the suspiciousareas may also be determined by manual discrimination.

In operation 530, the image data of the suspicious area may be obtained,and the pollution source may be determined through image recognition.

In some embodiments, the management platform may extract image featuresfrom the image data and combine other features to determine thepollution source through an emission determination model.

The pollution source identification model may be a model for judging thetype of pollution source. For example, the pollution sourceidentification model may be a convolutional neural network model, a deepneural network model, or the like, or any combination thereof.

In some embodiments, the pollution source identification model mayinclude an image feature extraction layer and a judgment layer.

The image feature extraction layer may be a model for extractingfeatures related to pollution sources in an image. For example, theimage feature extraction layer may be a convolutional neural networkmodel.

The input of the image feature extraction layer may include image dataof suspicious areas, and the output of the image feature extractionlayer may include an image feature vector. The image feature vector maybe a vector reflecting the features contained in the image. For example,elements of the image feature vector may include exhaust emissionfeatures of pollution sources, building features, scale features, etc.

The judgment layer may be a model for finally obtaining the types ofpollution sources. For example, the decision layer may be a deep neuralnetwork model.

The input of the judgment layer may include image feature vectors, andair quality data, and the output of the judgment layer may include thetype of pollution source. The types of the pollution source may be avehicle source (e.g., the exhaust emission of a vehicle, and the airpollution including the pollution of nitrogen dioxide, carbon monoxide,and PM 2.5), a dust source (e.g., construction site dust, an excavationoperation, a sandstorm, a straw burning, etc., pollution including PM2.5 and PM 10), an industrial source (e.g., factory exhaust gasemissions, air pollution including sulfur dioxide pollution), etc.

In some embodiments, the pollution source identification model may beobtained through joint training of the image feature extraction layerand the judgment layer. For example, training sample data is input intothe image feature extraction layer to obtain the output historical imagefeature vector, the training sample data being historical image data,then the above historical image feature vector and the historical airquality data used as the training sample data of the judgment layer isinput into the judgment layer to obtain the type of the pollutionsource, and the output of the judgment layer is verified by the type ofthe sample pollution source. The verification data of the historicalimage feature vector output by the image feature extraction layer isobtained by using the back-propagation features of the neural networkmodel, and the verification data is used as the label to train the imagefeature extraction layer.

For another example, the training sample data includes historical imagedata, historical image feature vector, and historical air quality data.The training sample data is input image feature extraction layer, samplehistorical image feature vector and historical air quality data areinput judgment layer and the output of the feature extraction layer isused as the input of the judgment layer, and the label is the type ofpollution source. During the training process, a loss function isestablished based on the type of pollution source and the output of theimage feature extraction layer to update the parameters of the model.

In some embodiments, the training sample data may include at leasthistorical image data and historical air quality data. The labels may betypes of pollution sources. The labels may be obtained by manualannotation or determined based on the actual types of pollution sources.

In some embodiments of the present disclosure, the suspicious area isdetermined based on the deviation data between the air pollutionsituation and the actual air pollution situation, the image data of thesuspicious area is identified by the image to determine the pollutionsources, and the emission determination model is combined with otherfeatures to determine the pollution source, which is beneficial toaccurately, objectively, intelligently, and efficiently determine theareas with additional pollution sources and avoid the threat ofpotential pollution sources.

It should be noted that the above description about the process 500 isonly for example and illustration, and not limit the scope ofapplication of the present disclosure. For those skilled in the art,various modifications and changes can be made to the process 500 underthe guidance of the present disclosure. However, these corrections andchanges are still within the scope of the present disclosure. Forexample, the process 500 may also include preprocessing operations.

Some embodiments of the present disclosure also disclose anon-transitory computer-readable storage medium for storing computerinstructions, when reading the computer instructions in the storagemedium, a computer implements the above-mentioned method for predictingair quality in a smart city based on the Internet of Things.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by the present disclosure,which are within the spirit and scope of the exemplary embodiments ofthe present disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment,” “one embodiment,” or “an alternativeembodiment” in various portions of the present disclosure are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or features may be combined as suitablein one or more embodiments of the present disclosure.

Additionally, the order in which elements and sequences of the processesdescribed herein are processed, the use of alphanumeric characters, orthe use of other designations, is not intended to limit the order of theprocesses and methods described herein, unless explicitly claimed. Whilevarious presently contemplated embodiments of the invention have beendiscussed in the foregoing disclosure by way of example, it is to beunderstood that such detail is solely for that purpose and that theappended claims are not limited to the disclosed embodiments, but, onthe contrary, are intended to cover all modifications and equivalentarrangements that are within the spirit and scope of the embodimentsherein. For example, although the system components described above maybe implemented by hardware devices, they may also be implemented bysoftware-only solutions, such as installing the described system on anexisting server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the present disclosureare to be understood as being modified in some instances by the term“about,” “approximate,” or “substantially.” For example, “about,”“approximate,” or “substantially” may indicate ±20% variation of thevalue it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the present disclosure are approximations, thenumerical values set forth in the specific examples are reported asprecisely as practicable.

For each patent, patent present disclosure, patent present disclosurepublications and other materials referenced in the present disclosure,such as articles, books, instructions, publications, documents, etc.,here, all of them will be incorporated herein by reference. Except forthe present disclosure history documentation of the present disclosureor the conflict, there is also an except for documents (current or afterthe present disclosure), which are available in the present disclosure.It should be noted that, if there is any inconsistency or conflictbetween the descriptions, definitions and/or use of terms in theaccompanying materials of the specification and the contents of thespecification, the descriptions, definitions and/or use of terms in thisspecification shall prevail.

Finally, it should be understood that the embodiments described in thepresent disclosure are intended to illustrate the principles of theembodiments of the present disclosure. Other deformations may alsobelong to the scope of the present disclosure. Thus, as an example, notlimited, the alternative configuration of the present disclosureembodiment may be consistent with the teachings of the presentdisclosure. Accordingly, the embodiments of the present disclosure arenot limited to the embodiments of the present disclosure clearlydescribed and described.

What is claimed is:
 1. A method for area management in a smart citybased on an Internet of Things, wherein the method is executed by amanagement platform, and the method comprises: obtaining environmentalmonitoring data in a target area through a sensor network platform,wherein the environmental monitoring data includes at least one of airquality data, weather data, and satellite image data; predicting an airpollution situation in the target area through a regional predictionmodel based on the environmental monitoring data, wherein the regionalprediction model is a machine learning model; and sending promptinformation, which is determined based on the air pollution situation inthe target area, to a user platform through a service platform, whereinthe target area is a hexagonal area, and an input of the regionalprediction model includes features of the hexagonal area and otherhexagonal areas adjacent to the hexagonal area; the features includetime-series features of the hexagonal area and the other hexagonalareas, and the time-series features include a time feature, a trafficfeature, and a meteorological feature; the regional prediction modelincludes seven regional models and an air quality determination model,the seven regional models respectively correspond to the hexagonal areaand the other hexagonal areas adjacent to the hexagonal area, each ofthe seven regional models processes the time-series feature of thecorresponding hexagonal area to output a corresponding time-seriesfeature vector, and the air quality determination model processes thetime-series feature vectors output by the seven regional models todetermine the pollution situation of the target area; wherein aprediction mode of the regional prediction model is an iterativeprediction, and the iterative prediction includes: iterativelypredicting air pollution situation in the hexagonal area and the otherhexagonal areas adjacent to the hexagonal area at a second time pointthrough the regional prediction model based on air pollution situationin the hexagonal area and the other hexagonal areas adjacent to thehexagonal area at a first time point; and the regional prediction modelis obtained by joint training based on training data and a label,wherein the training data includes sample time-series features of asample hexagonal area and other sample hexagonal areas adjacent to thesample hexagonal area and sample time-series feature vectors, and thelabel is an actual air quality in a sample target area.
 2. The methodaccording to claim 1, wherein the sensor network platform is composed ofat least one sensor sub-platform, and the sensor network platform isconfigured to: respectively obtaining sensing data collected bydifferent types of sensor devices configured on an object platformthrough the at least one sensor sub-platform, wherein the sensing datais used as one of components of the environmental monitoring data; anduploading the sensing data to the management platform after summarizingand processing.
 3. The method according to claim 1, wherein the input ofthe regional prediction model further includes vehicle pollution data,and the vehicle pollution data is obtained by operations including:obtaining road situation prediction data and vehicle registration datain the hexagonal area and other hexagonal areas adjacent to thehexagonal area; and predicting the vehicle pollution data in thehexagonal area and other hexagonal areas adjacent to the hexagonal areaby processing the road situation prediction data and the vehicleregistration data through a vehicle pollution model.
 4. The methodaccording to claim 1, wherein the method further includes: obtainingdeviation data between the air pollution situation and an actual airpollution situation; and determining a suspicious area based on thedeviation data, wherein the suspicious area is an area that includesadditional pollution sources.
 5. The method according to claim 4,wherein the pollution source is determined based on a pollution sourceidentification model; and the method also includes: obtaining image dataof the suspicious area; and processing the image data of the suspiciousarea by the pollution source identification model to determine thepollution source.
 6. The method according to claim 5, wherein thepollution source identification model includes an image featureextraction layer and a judgment layer; the image feature extractionlayer extracts an image feature vector of the suspicious area based onthe image data of the suspicious area; and the judgment layer determinesthe pollution source based on the image feature vector and air qualityfeatures of the suspicious area.
 7. The method according to claim 1,wherein thein the target area sending prompt information, which isdetermined based on the air pollution situation in the target area, to auser platform through a service platform includes: determining a targetsub-area wherein the air pollution situation in the target areasatisfies a preset condition; obtaining vehicle information entering thetarget sub-area through an object platform; and sending the promptinformation and preferential information through the service platformbased on the vehicle information and the air pollution situation in thetarget area.
 8. The method according to claim 1, wherein training of theregional prediction model includes: inputting the sample time-seriesfeatures of the sample hexagonal area and the other sample hexagonalareas adjacent to the sample hexagonal area into seven preliminaryregional models to output the sample time-series feature vectors;inputting the sample time-series feature vectors into a preliminary airquality determination model to output an air pollution situation in thesample target area; and performing verification on the air pollutionsituation in the sample target area output by the preliminary airquality determination model based on the label.
 9. A system for areamanagement in a smart city based on an Internet of Things, comprising auser platform, a service platform, a management platform, a sensornetwork platform, and an object platform, wherein the sensor networkplatform is configured to obtain environmental monitoring data in atarget area through the sensor network platform, wherein theenvironmental monitoring data includes at least one air quality data,weather data, and satellite image data; the management platform isconfigured to predict an air pollution situation in the target areathrough a regional prediction model based on the environmentalmonitoring data, wherein the regional prediction model is a machinelearning model; and the service platform is configured to send promptinformation, which is determined based on the air pollution situation inthe target area, to the user platform, wherein the target area is ahexagonal area, and an input of the regional prediction model includesfeatures of the hexagonal area and other hexagonal areas adjacent to thehexagonal area; the features include time-series features of thehexagonal area and the other hexagonal areas, and the time-seriesfeatures include a time feature, a traffic feature, and a meteorologicalfeature; the regional prediction model includes seven regional modelsand an air quality determination model, the seven regional modelsrespectively correspond to the hexagonal area and the other hexagonalareas adjacent to the hexagonal area, each of the seven regional modelsprocesses the time-series feature of the corresponding hexagonal area tooutput a corresponding time-series feature vector, and the air qualitydetermination model processes the time-series feature vectors output bythe seven regional models to determine the pollution situation of thetarget area; wherein a prediction mode of the regional prediction modelis an iterative prediction, and the iterative prediction includes:iteratively predicting air pollution situation in the hexagonal area andthe other hexagonal areas adjacent to the hexagonal area at a secondtime point through the regional prediction model based on air pollutionsituation in the hexagonal area and the other hexagonal areas adjacentto the hexagonal area at a first time point; and the regional predictionmodel is obtained by joint training based on training data and a label,wherein the training data includes sample time-series features of asample hexagonal area and other sample hexagonal areas adjacent to thesample hexagonal area and sample time-series feature vectors, and thelabel is an actual air quality in a sample target area.
 10. The systemaccording to claim 9, wherein the sensor network platform is composed ofat least one sensor sub-platform, and the sensor network platform isconfigured to: respectively obtain sensing data collected by differenttypes of sensor devices configured on the object platform through the atleast one sensor sub-platform, wherein the sensing data is used as oneof components of the environmental monitoring data; and upload thesensing data to the management platform after summarizing andprocessing.
 11. The system according to claim 9, wherein the input ofthe regional prediction model further includes vehicle pollution data,the vehicle pollution data is obtained by the management platform, andthe management platform is further configured to: obtain road situationprediction data and vehicle registration data in the hexagonal area andother hexagonal areas adjacent to the hexagonal area; and predict thevehicle pollution data in the hexagonal area and other hexagonal areasadjacent to the hexagonal area by processing the road situationprediction data and the vehicle registration data through a vehiclepollution model.
 12. The system according to claim 9, wherein themanagement platform is further configured to: obtain deviation databetween the air pollution situation and an actual air pollutionsituation; and determine a suspicious area based on the deviation data,wherein the suspicious area is an area that includes additionalpollution sources.
 13. The system according to claim 12, wherein themanagement platform is further configured to: obtain image data of thesuspicious area; and process the image data of the suspicious area bythe pollution source identification model to determine the pollutionsource.
 14. The system according to claim 12, wherein the pollutionsource identification model includes an image feature extraction layerand a judgment layer; the image feature extraction layer extracts animage feature vector of the suspicious area based on the image data ofthe suspicious area; and the judgment layer determines the pollutionsource based on the image feature vector and air quality features of thesuspicious area.
 15. A non-transitory computer-readable storage mediumstoring computer instructions, wherein when reading the computerinstructions in the storage medium, a computer implements the method ofclaim 1.